In [13]:
import pandas as pd
from sklearn.model_selection import train_test_split
import joblib
import numpy as np
from sklearn.feature_selection import VarianceThreshold, SelectKBest, f_classif
In [14]:
# Show all columns in the DataFrame
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
In [15]:
# Load raw data
#benign = pd.read_csv('datasets/hmi_normal_6h_flow_label.csv')
scapy_modify = pd.read_csv('datasets/mitm_hmi_scapy_modify_1plc_6h_flow_label.csv')
fw_36plc = pd.read_csv('datasets/mitm_hmi_forward_36plc_6h_flow_label.csv')
In [16]:
scapy_modify.groupby("Label").describe()
Out[16]:
| Src Port | Dst Port | Protocol | Flow Duration | Tot Fwd Pkts | Tot Bwd Pkts | TotLen Fwd Pkts | TotLen Bwd Pkts | Fwd Pkt Len Max | Fwd Pkt Len Min | Fwd Pkt Len Mean | Fwd Pkt Len Std | Bwd Pkt Len Max | Bwd Pkt Len Min | Bwd Pkt Len Mean | Bwd Pkt Len Std | Flow Byts/s | Flow Pkts/s | Flow IAT Mean | Flow IAT Std | Flow IAT Max | Flow IAT Min | Fwd IAT Tot | Fwd IAT Mean | Fwd IAT Std | Fwd IAT Max | Fwd IAT Min | Bwd IAT Tot | Bwd IAT Mean | Bwd IAT Std | Bwd IAT Max | Bwd IAT Min | Fwd PSH Flags | Bwd PSH Flags | Fwd URG Flags | Bwd URG Flags | Fwd Header Len | Bwd Header Len | Fwd Pkts/s | Bwd Pkts/s | Pkt Len Min | Pkt Len Max | Pkt Len Mean | Pkt Len Std | Pkt Len Var | FIN Flag Cnt | SYN Flag Cnt | RST Flag Cnt | PSH Flag Cnt | ACK Flag Cnt | URG Flag Cnt | CWE Flag Count | ECE Flag Cnt | Down/Up Ratio | Pkt Size Avg | Fwd Seg Size Avg | Bwd Seg Size Avg | Fwd Byts/b Avg | Fwd Pkts/b Avg | Fwd Blk Rate Avg | Bwd Byts/b Avg | Bwd Pkts/b Avg | Bwd Blk Rate Avg | Subflow Fwd Pkts | Subflow Fwd Byts | Subflow Bwd Pkts | Subflow Bwd Byts | Init Fwd Win Byts | Init Bwd Win Byts | Fwd Act Data Pkts | Fwd Seg Size Min | Active Mean | Active Std | Active Max | Active Min | Idle Mean | Idle Std | Idle Max | Idle Min | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | |
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| mitm | 6754.0 | 45706.734972 | 5553.484326 | 32784.0 | 44818.0 | 44818.0 | 45037.0 | 60992.0 | 6754.0 | 45878.698253 | 6111.837581 | 32784.0 | 44818.0 | 44818.0 | 47685.5 | 60992.0 | 6754.0 | 6.0 | 0.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6754.0 | 434630.275540 | 1.566391e+06 | 14.0 | 35.0 | 616202.5 | 792774.25 | 99088176.0 | 6754.0 | 2.449067 | 2.457426 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 6754.0 | 3.553154 | 1.569620 | 1.0 | 2.0 | 5.0 | 5.0 | 8.0 | 6754.0 | 47.787385 | 48.099579 | 0.0 | 0.0 | 72.0 | 98.0 | 168.0 | 6754.0 | 38.269174 | 38.660947 | 0.0 | 0.0 | 72.0 | 72.0 | 100.0 | 6754.0 | 33.752147 | 34.166059 | 0.0 | 0.0 | 44.0 | 70.0 | 72.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 9.749585 | 9.755739 | 0.0 | 0.0 | 18.0 | 19.6 | 28.0 | 6754.0 | 14.920295 | 15.028194 | 0.0 | 0.0 | 21.786846 | 30.672463 | 34.292856 | 6754.0 | 24.265028 | 24.886134 | 0.0 | 0.0 | 44.0 | 44.0 | 72.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 7.480942 | 7.496550 | 0.0 | 0.0 | 12.375 | 14.4 | 16.666667 | 6754.0 | 11.012655 | 11.146532 | 0.0 | 0.0 | 20.51341 | 20.513410 | 29.330303 | 6754.0 | 106.448680 | 107.724388 | 0.0 | 0.0 | 1.992790 | 216.632122 | 338.550777 | 6754.0 | 35418.533351 | 39916.649455 | 0.121104 | 12.613931 | 16.231172 | 57142.857143 | 142857.142857 | 6754.0 | 47810.307408 | 144692.168035 | 14.0 | 35.0 | 69502.5 | 88088.583333 | 9.008016e+06 | 6754.0 | 61929.003762 | 455455.016393 | 0.0 | 0.0 | 75823.085052 | 105804.078147 | 2.963342e+07 | 6754.0 | 165512.691146 | 1.508231e+06 | 14.0 | 35.0 | 194066.0 | 276949.75 | 98355743.0 | 6754.0 | 63.708617 | 1664.419176 | 13.0 | 20.0 | 29.0 | 38.0 | 100501.0 | 6754.0 | 314562.587060 | 315999.420297 | 0.0 | 0.0 | 470988.5 | 628555.0 | 776518.0 | 6754.0 | 81354.608528 | 82457.804395 | 0.0 | 0.0 | 118766.25 | 158968.375 | 252569.00 | 6754.0 | 74244.508187 | 75455.695403 | 0.0 | 0.0 | 65767.341155 | 151067.292561 | 201558.904417 | 6754.0 | 154264.025318 | 155730.636886 | 0.0 | 0.0 | 204687.0 | 307223.25 | 438777.0 | 6754.0 | 6640.931744 | 18928.241434 | 0.0 | 0.0 | 1958.0 | 6095.25 | 135708.0 | 6754.0 | 431217.406574 | 1.565578e+06 | 0.0 | 35.0 | 609929.0 | 785992.25 | 99088176.0 | 6754.0 | 102895.405747 | 237557.336597 | 0.0 | 35.0 | 126171.9 | 194004.9125 | 1.415545e+07 | 6754.0 | 60112.545143 | 569092.745766 | 0.0 | 0.0 | 57030.169689 | 98073.563059 | 3.712901e+07 | 6754.0 | 173616.759994 | 1.508831e+06 | 0.0 | 35.0 | 213070.0 | 293905.25 | 98355743.0 | 6754.0 | 38636.816849 | 43667.114064 | 0.0 | 35.0 | 105.5 | 81205.25 | 151454.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 78.790643 | 78.922803 | 0.0 | 0.0 | 136.0 | 160.0 | 192.0 | 6754.0 | 121.288126 | 57.548323 | 44.0 | 64.0 | 176.0 | 176.0 | 264.0 | 6754.0 | 3.035348 | 3.084742 | 0.0 | 0.0 | 3.770851 | 6.213025 | 15.614265 | 6754.0 | 35415.498003 | 39919.342230 | 0.080736 | 6.368036 | 9.511096 | 57142.857143 | 142857.142857 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 35.010364 | 35.021639 | 0.0 | 0.0 | 68.0 | 70.0 | 72.0 | 6754.0 | 7.821808 | 7.821432 | 0.0 | 0.0 | 13.153846 | 15.636364 | 20.000000 | 6754.0 | 12.154557 | 12.152875 | 0.0 | 0.0 | 22.905756 | 24.371369 | 27.804512 | 6754.0 | 295.403750 | 295.439760 | 0.0 | 0.0 | 524.717949 | 593.963636 | 773.090909 | 6754.0 | 0.499704 | 0.500037 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 6754.0 | 0.499852 | 0.500037 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.500148 | 0.500037 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.500444 | 0.500629 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 | 6754.0 | 8.603843 | 8.603407 | 0.0 | 0.0 | 14.25 | 17.2 | 21.818182 | 6754.0 | 9.749585 | 9.755739 | 0.0 | 0.0 | 18.0 | 19.6 | 28.0 | 6754.0 | 7.480942 | 7.496550 | 0.0 | 0.0 | 12.375 | 14.4 | 16.666667 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 2.449067 | 2.457426 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 6754.0 | 47.787385 | 48.099579 | 0.0 | 0.0 | 72.0 | 98.0 | 168.0 | 6754.0 | 3.553154 | 1.569620 | 1.0 | 2.0 | 5.0 | 5.0 | 8.0 | 6754.0 | 38.269174 | 38.660947 | 0.0 | 0.0 | 72.0 | 72.0 | 100.0 | 6754.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 6754.0 | 83.896062 | 0.994657 | 83.0 | 83.0 | 83.0 | 85.0 | 85.0 | 6754.0 | 1.000740 | 1.000740 | 0.0 | 0.0 | 2.0 | 2.0 | 3.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.008884 | 0.549823 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.008884 | 0.549823 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | 6754.0 | 0.008884 | 0.549823 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | 6754.0 | 25647.077584 | 1.503937e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 98355743.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 25647.077584 | 1.503937e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 98355743.0 | 6754.0 | 25647.077584 | 1.503937e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 98355743.0 |
| normal | 164457.0 | 45785.487161 | 5607.404711 | 32768.0 | 44818.0 | 44818.0 | 45764.0 | 60998.0 | 164457.0 | 45946.057510 | 6071.042539 | 32768.0 | 44818.0 | 44818.0 | 48036.0 | 60998.0 | 164457.0 | 6.0 | 0.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 164457.0 | 58148.334805 | 1.424615e+06 | 7.0 | 14.0 | 57859.0 | 73380.00 | 117511126.0 | 164457.0 | 2.459804 | 2.466897 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 164457.0 | 3.541515 | 1.553819 | 2.0 | 2.0 | 5.0 | 5.0 | 8.0 | 164457.0 | 47.981515 | 48.193465 | 0.0 | 0.0 | 72.0 | 96.0 | 100.0 | 164457.0 | 38.461665 | 38.756286 | 0.0 | 0.0 | 72.0 | 78.0 | 100.0 | 164457.0 | 33.978535 | 34.284204 | 0.0 | 0.0 | 44.0 | 68.0 | 72.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 9.749339 | 9.753269 | 0.0 | 0.0 | 18.0 | 19.2 | 20.0 | 164457.0 | 14.990621 | 15.069873 | 0.0 | 0.0 | 21.786846 | 29.852973 | 31.496031 | 164457.0 | 24.458685 | 24.926832 | 0.0 | 0.0 | 44.0 | 50.0 | 72.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 7.558702 | 7.572162 | 0.0 | 0.0 | 12.000 | 15.6 | 16.666667 | 164457.0 | 11.102558 | 11.203735 | 0.0 | 0.0 | 20.51341 | 22.733236 | 29.330303 | 164457.0 | 1164.924351 | 1180.243370 | 0.0 | 0.0 | 1.803155 | 2355.486778 | 3057.083121 | 164457.0 | 71186.095216 | 78249.038332 | 0.102118 | 136.278772 | 172.962502 | 142857.142857 | 285714.285714 | 164457.0 | 6053.155641 | 129522.082678 | 7.0 | 14.0 | 6424.0 | 8153.222222 | 1.068283e+07 | 164457.0 | 13498.581455 | 429158.498273 | 0.0 | 0.0 | 11627.964869 | 13719.697409 | 3.540754e+07 | 164457.0 | 43585.339639 | 1.423458e+06 | 7.0 | 14.0 | 36986.0 | 43238.00 | 117440559.0 | 164457.0 | 16.784807 | 9.151835 | 4.0 | 11.0 | 14.0 | 22.0 | 2064.0 | 164457.0 | 34062.665639 | 34647.273340 | 0.0 | 0.0 | 50543.0 | 66317.0 | 156975.0 | 164457.0 | 8727.146386 | 8898.843536 | 0.0 | 0.0 | 13176.00 | 16922.000 | 39243.75 | 164457.0 | 10256.294977 | 10501.460605 | 0.0 | 0.0 | 14713.764316 | 19285.950368 | 68549.944913 | 164457.0 | 23079.325629 | 23513.684397 | 0.0 | 0.0 | 37016.0 | 43284.00 | 141872.0 | 164457.0 | 1221.682902 | 1431.210742 | 0.0 | 0.0 | 1239.0 | 2082.00 | 19406.0 | 164457.0 | 55162.797917 | 1.424580e+06 | 7.0 | 14.0 | 54144.0 | 67067.00 | 117511126.0 | 164457.0 | 11443.127877 | 203580.924270 | 7.0 | 14.0 | 11946.6 | 16542.2500 | 1.678730e+07 | 164457.0 | 17682.778109 | 537956.338481 | 0.0 | 0.0 | 14333.896201 | 18761.503636 | 4.438392e+07 | 164457.0 | 43794.498726 | 1.423459e+06 | 7.0 | 14.0 | 37471.0 | 43664.00 | 117440559.0 | 164457.0 | 1716.652025 | 2087.730052 | 7.0 | 14.0 | 103.0 | 3182.00 | 13849.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 79.039846 | 79.159871 | 0.0 | 0.0 | 136.0 | 160.0 | 192.0 | 164457.0 | 121.004080 | 57.193933 | 64.0 | 64.0 | 176.0 | 176.0 | 264.0 | 164457.0 | 33.152261 | 33.689838 | 0.0 | 0.0 | 0.041677 | 67.281168 | 96.677516 | 164457.0 | 71152.942954 | 78279.141975 | 0.068079 | 68.809865 | 100.446989 | 142857.142857 | 285714.285714 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 34.897913 | 34.918514 | 0.0 | 0.0 | 66.0 | 68.0 | 72.0 | 164457.0 | 7.857901 | 7.856737 | 0.0 | 0.0 | 13.384615 | 15.636364 | 15.818182 | 164457.0 | 12.197651 | 12.195516 | 0.0 | 0.0 | 23.027297 | 24.371369 | 24.491928 | 164457.0 | 297.512394 | 297.490619 | 0.0 | 0.0 | 530.256410 | 593.963636 | 599.854545 | 164457.0 | 0.500106 | 0.500002 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 164457.0 | 0.499894 | 0.500002 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.500106 | 0.500002 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.500283 | 0.500427 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 | 164457.0 | 8.643638 | 8.642374 | 0.0 | 0.0 | 14.50 | 17.2 | 17.400000 | 164457.0 | 9.749339 | 9.753269 | 0.0 | 0.0 | 18.0 | 19.2 | 20.0 | 164457.0 | 7.558702 | 7.572162 | 0.0 | 0.0 | 12.000 | 15.6 | 16.666667 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 2.459804 | 2.466897 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 164457.0 | 47.981515 | 48.193465 | 0.0 | 0.0 | 72.0 | 96.0 | 100.0 | 164457.0 | 3.541515 | 1.553819 | 2.0 | 2.0 | 5.0 | 5.0 | 8.0 | 164457.0 | 38.461665 | 38.756286 | 0.0 | 0.0 | 72.0 | 78.0 | 100.0 | 164457.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 164457.0 | 83.918684 | 0.996691 | 83.0 | 83.0 | 83.0 | 85.0 | 85.0 | 164457.0 | 1.000213 | 1.000003 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.003867 | 0.287735 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.003867 | 0.287735 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 164457.0 | 0.003867 | 0.287735 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 164457.0 | 20526.033231 | 1.423597e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 117440559.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 20526.033231 | 1.423597e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 117440559.0 | 164457.0 | 20526.033231 | 1.423597e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 117440559.0 |
In [17]:
fw_36plc['Label'].unique()
Out[17]:
array(['mitm'], dtype=object)
In [18]:
fw_36plc['Label'] = 'mitm_36plc'
In [19]:
# Drop unnecessary columns
cols_to_drop = ['Flow ID', 'Src IP', 'Src Port', 'Dst IP', 'Dst Port', 'Timestamp']
#benign.drop(columns=cols_to_drop, errors='ignore', inplace=True)
scapy_modify.drop(columns=cols_to_drop, errors='ignore', inplace=True)
fw_36plc.drop(columns=cols_to_drop,errors='ignore', inplace=True)
In [20]:
# Merge
df = pd.concat([scapy_modify,fw_36plc], ignore_index=True)
In [21]:
df.groupby("Label").describe()
Out[21]:
| Protocol | Flow Duration | Tot Fwd Pkts | Tot Bwd Pkts | TotLen Fwd Pkts | TotLen Bwd Pkts | Fwd Pkt Len Max | Fwd Pkt Len Min | Fwd Pkt Len Mean | Fwd Pkt Len Std | Bwd Pkt Len Max | Bwd Pkt Len Min | Bwd Pkt Len Mean | Bwd Pkt Len Std | Flow Byts/s | Flow Pkts/s | Flow IAT Mean | Flow IAT Std | Flow IAT Max | Flow IAT Min | Fwd IAT Tot | Fwd IAT Mean | Fwd IAT Std | Fwd IAT Max | Fwd IAT Min | Bwd IAT Tot | Bwd IAT Mean | Bwd IAT Std | Bwd IAT Max | Bwd IAT Min | Fwd PSH Flags | Bwd PSH Flags | Fwd URG Flags | Bwd URG Flags | Fwd Header Len | Bwd Header Len | Fwd Pkts/s | Bwd Pkts/s | Pkt Len Min | Pkt Len Max | Pkt Len Mean | Pkt Len Std | Pkt Len Var | FIN Flag Cnt | SYN Flag Cnt | RST Flag Cnt | PSH Flag Cnt | ACK Flag Cnt | URG Flag Cnt | CWE Flag Count | ECE Flag Cnt | Down/Up Ratio | Pkt Size Avg | Fwd Seg Size Avg | Bwd Seg Size Avg | Fwd Byts/b Avg | Fwd Pkts/b Avg | Fwd Blk Rate Avg | Bwd Byts/b Avg | Bwd Pkts/b Avg | Bwd Blk Rate Avg | Subflow Fwd Pkts | Subflow Fwd Byts | Subflow Bwd Pkts | Subflow Bwd Byts | Init Fwd Win Byts | Init Bwd Win Byts | Fwd Act Data Pkts | Fwd Seg Size Min | Active Mean | Active Std | Active Max | Active Min | Idle Mean | Idle Std | Idle Max | Idle Min | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | |
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| mitm | 6754.0 | 6.0 | 0.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6754.0 | 434630.275540 | 1.566391e+06 | 14.0 | 35.0 | 616202.5 | 792774.25 | 99088176.0 | 6754.0 | 2.449067 | 2.457426 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 6754.0 | 3.553154 | 1.569620 | 1.0 | 2.0 | 5.0 | 5.0 | 8.0 | 6754.0 | 47.787385 | 48.099579 | 0.0 | 0.0 | 72.0 | 98.0 | 168.0 | 6754.0 | 38.269174 | 38.660947 | 0.0 | 0.0 | 72.0 | 72.0 | 100.0 | 6754.0 | 33.752147 | 34.166059 | 0.0 | 0.0 | 44.0 | 70.0 | 72.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 9.749585 | 9.755739 | 0.0 | 0.0 | 18.0 | 19.6 | 28.0 | 6754.0 | 14.920295 | 15.028194 | 0.0 | 0.0 | 21.786846 | 30.672463 | 34.292856 | 6754.0 | 24.265028 | 24.886134 | 0.0 | 0.0 | 44.0 | 44.0 | 72.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 7.480942 | 7.496550 | 0.0 | 0.0 | 12.375 | 14.4 | 16.666667 | 6754.0 | 11.012655 | 11.146532 | 0.0 | 0.0 | 20.51341 | 20.513410 | 29.330303 | 6754.0 | 106.448680 | 107.724388 | 0.0 | 0.0 | 1.992790 | 216.632122 | 338.550777 | 6754.0 | 35418.533351 | 39916.649455 | 0.121104 | 12.613931 | 16.231172 | 57142.857143 | 142857.142857 | 6754.0 | 47810.307408 | 144692.168035 | 14.0 | 35.0 | 69502.500000 | 88088.583333 | 9.008016e+06 | 6754.0 | 61929.003762 | 455455.016393 | 0.0 | 0.0 | 75823.085052 | 105804.078147 | 2.963342e+07 | 6754.0 | 165512.691146 | 1.508231e+06 | 14.0 | 35.0 | 194066.0 | 276949.75 | 98355743.0 | 6754.0 | 63.708617 | 1664.419176 | 13.0 | 20.0 | 29.0 | 38.0 | 100501.0 | 6754.0 | 314562.587060 | 315999.420297 | 0.0 | 0.0 | 470988.5 | 628555.0 | 776518.0 | 6754.0 | 81354.608528 | 82457.804395 | 0.0 | 0.0 | 118766.25 | 158968.375 | 252569.00 | 6754.0 | 74244.508187 | 75455.695403 | 0.0 | 0.0 | 65767.341155 | 151067.292561 | 201558.904417 | 6754.0 | 154264.025318 | 155730.636886 | 0.0 | 0.0 | 204687.0 | 307223.25 | 438777.0 | 6754.0 | 6640.931744 | 18928.241434 | 0.0 | 0.0 | 1958.0 | 6095.25 | 135708.0 | 6754.0 | 431217.406574 | 1.565578e+06 | 0.0 | 35.0 | 609929.0 | 785992.25 | 99088176.0 | 6754.0 | 102895.405747 | 237557.336597 | 0.0 | 35.0 | 126171.9 | 194004.9125 | 1.415545e+07 | 6754.0 | 60112.545143 | 569092.745766 | 0.0 | 0.0 | 57030.169689 | 98073.563059 | 3.712901e+07 | 6754.0 | 173616.759994 | 1.508831e+06 | 0.0 | 35.0 | 213070.0 | 293905.25 | 98355743.0 | 6754.0 | 38636.816849 | 43667.114064 | 0.0 | 35.0 | 105.5 | 81205.25 | 151454.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 78.790643 | 78.922803 | 0.0 | 0.0 | 136.0 | 160.0 | 192.0 | 6754.0 | 121.288126 | 57.548323 | 44.0 | 64.0 | 176.0 | 176.0 | 264.0 | 6754.0 | 3.035348 | 3.084742 | 0.0 | 0.0 | 3.770851 | 6.213025 | 15.614265 | 6754.0 | 35415.498003 | 39919.342230 | 0.080736 | 6.368036 | 9.511096 | 57142.857143 | 142857.142857 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 35.010364 | 35.021639 | 0.0 | 0.0 | 68.0 | 70.0 | 72.0 | 6754.0 | 7.821808 | 7.821432 | 0.0 | 0.0 | 13.153846 | 15.636364 | 20.000000 | 6754.0 | 12.154557 | 12.152875 | 0.0 | 0.0 | 22.905756 | 24.371369 | 27.804512 | 6754.0 | 295.403750 | 295.439760 | 0.0 | 0.0 | 524.717949 | 593.963636 | 773.090909 | 6754.0 | 0.499704 | 0.500037 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 6754.0 | 0.499852 | 0.500037 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.500148 | 0.500037 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.500444 | 0.500629 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 | 6754.0 | 8.603843 | 8.603407 | 0.0 | 0.0 | 14.25 | 17.2 | 21.818182 | 6754.0 | 9.749585 | 9.755739 | 0.0 | 0.0 | 18.0 | 19.6 | 28.0 | 6754.0 | 7.480942 | 7.496550 | 0.0 | 0.0 | 12.375 | 14.4 | 16.666667 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 2.449067 | 2.457426 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 6754.0 | 47.787385 | 48.099579 | 0.0 | 0.0 | 72.0 | 98.0 | 168.0 | 6754.0 | 3.553154 | 1.569620 | 1.0 | 2.0 | 5.0 | 5.0 | 8.0 | 6754.0 | 38.269174 | 38.660947 | 0.0 | 0.0 | 72.0 | 72.0 | 100.0 | 6754.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 6754.0 | 83.896062 | 0.994657 | 83.0 | 83.0 | 83.0 | 85.0 | 85.0 | 6754.0 | 1.000740 | 1.000740 | 0.0 | 0.0 | 2.0 | 2.0 | 3.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.008884 | 0.549823 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 0.008884 | 0.549823 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | 6754.0 | 0.008884 | 0.549823 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | 6754.0 | 25647.077584 | 1.503937e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 98355743.0 | 6754.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6754.0 | 25647.077584 | 1.503937e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 98355743.0 | 6754.0 | 25647.077584 | 1.503937e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 98355743.0 |
| mitm_36plc | 188383.0 | 6.0 | 0.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 188383.0 | 65592.658149 | 1.536137e+06 | 5.0 | 11.0 | 60558.0 | 78157.50 | 119999986.0 | 188383.0 | 2.456639 | 2.464104 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 188383.0 | 3.545108 | 1.558325 | 2.0 | 2.0 | 5.0 | 5.0 | 8.0 | 188383.0 | 47.905904 | 48.129617 | 0.0 | 0.0 | 72.0 | 96.0 | 100.0 | 188383.0 | 38.543924 | 38.853495 | 0.0 | 0.0 | 72.0 | 78.0 | 100.0 | 188383.0 | 33.901817 | 34.226893 | 0.0 | 0.0 | 44.0 | 68.0 | 72.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 9.746797 | 9.750116 | 0.0 | 0.0 | 18.0 | 19.2 | 25.0 | 188383.0 | 14.965210 | 15.048824 | 0.0 | 0.0 | 21.786846 | 29.852973 | 34.000000 | 188383.0 | 24.539836 | 25.033469 | 0.0 | 0.0 | 44.0 | 50.0 | 72.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 7.563971 | 7.577124 | 0.0 | 0.0 | 12.000 | 15.6 | 16.666667 | 188383.0 | 11.127344 | 11.233186 | 0.0 | 0.0 | 20.51341 | 22.733236 | 29.330303 | 188383.0 | 1094.954881 | 1109.816059 | 0.0 | 0.0 | 2.007809 | 2210.650760 | 2991.746905 | 188383.0 | 90833.995794 | 99095.427321 | 0.100000 | 127.946774 | 165.130949 | 181818.181818 | 400000.000000 | 188383.0 | 6776.849563 | 139659.181256 | 5.0 | 11.0 | 6728.666667 | 8684.166667 | 1.090909e+07 | 188383.0 | 15207.344502 | 462689.885739 | 0.0 | 0.0 | 11859.989497 | 14094.021412 | 3.615716e+07 | 188383.0 | 49186.555385 | 1.534704e+06 | 5.0 | 11.0 | 37497.0 | 44321.00 | 119927029.0 | 188383.0 | 12.346714 | 5.497144 | 4.0 | 8.0 | 11.0 | 16.0 | 406.0 | 188383.0 | 34614.072215 | 35220.837825 | 0.0 | 0.0 | 51083.0 | 67361.0 | 229220.0 | 188383.0 | 8885.135330 | 9062.045093 | 0.0 | 0.0 | 13306.00 | 17228.750 | 57305.00 | 188383.0 | 10548.331855 | 10815.442744 | 0.0 | 0.0 | 15065.157716 | 19836.705001 | 99892.936230 | 188383.0 | 23655.525148 | 24129.986474 | 0.0 | 0.0 | 37500.0 | 44366.00 | 206711.0 | 188383.0 | 1217.281368 | 1417.517579 | 0.0 | 0.0 | 1204.0 | 2085.00 | 16531.0 | 188383.0 | 62640.287871 | 1.536110e+06 | 5.0 | 11.0 | 56631.0 | 72129.00 | 119999986.0 | 188383.0 | 12749.303370 | 219509.307229 | 5.0 | 11.0 | 12548.0 | 17757.5000 | 1.714286e+07 | 188383.0 | 19601.336573 | 579979.910079 | 0.0 | 0.0 | 14100.013163 | 18751.943017 | 4.532356e+07 | 188383.0 | 49405.281543 | 1.534704e+06 | 5.0 | 11.0 | 37921.0 | 44752.50 | 119927029.0 | 188383.0 | 1750.352845 | 2193.890666 | 5.0 | 11.0 | 65.0 | 2841.00 | 14882.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 78.965214 | 79.089035 | 0.0 | 0.0 | 136.0 | 160.0 | 192.0 | 188383.0 | 121.093007 | 57.296271 | 64.0 | 64.0 | 176.0 | 176.0 | 264.0 | 188383.0 | 31.116261 | 31.636463 | 0.0 | 0.0 | 0.046454 | 62.969913 | 87.624500 | 188383.0 | 90802.879533 | 99123.910254 | 0.066667 | 64.648763 | 95.632770 | 181818.181818 | 400000.000000 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 34.898733 | 34.916479 | 0.0 | 0.0 | 66.0 | 68.0 | 72.0 | 188383.0 | 7.858376 | 7.856614 | 0.0 | 0.0 | 13.384615 | 15.636364 | 17.200000 | 188383.0 | 12.198329 | 12.195228 | 0.0 | 0.0 | 23.027297 | 24.371369 | 25.231374 | 188383.0 | 297.522027 | 297.476984 | 0.0 | 0.0 | 530.256410 | 593.963636 | 636.622222 | 188383.0 | 0.500141 | 0.500001 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 188383.0 | 0.499859 | 0.500001 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.500141 | 0.500001 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.500422 | 0.500574 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 | 188383.0 | 8.644150 | 8.642232 | 0.0 | 0.0 | 14.50 | 17.2 | 19.111111 | 188383.0 | 9.746797 | 9.750116 | 0.0 | 0.0 | 18.0 | 19.2 | 25.0 | 188383.0 | 7.563971 | 7.577124 | 0.0 | 0.0 | 12.000 | 15.6 | 16.666667 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 2.456639 | 2.464104 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 188383.0 | 47.905904 | 48.129617 | 0.0 | 0.0 | 72.0 | 96.0 | 100.0 | 188383.0 | 3.545108 | 1.558325 | 2.0 | 2.0 | 5.0 | 5.0 | 8.0 | 188383.0 | 38.543924 | 38.853495 | 0.0 | 0.0 | 72.0 | 78.0 | 100.0 | 188383.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 188383.0 | 83.912099 | 0.996132 | 83.0 | 83.0 | 83.0 | 85.0 | 85.0 | 188383.0 | 1.000292 | 1.000003 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.003668 | 0.236835 | 0.0 | 0.0 | 0.0 | 0.0 | 27.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 0.003668 | 0.236835 | 0.0 | 0.0 | 0.0 | 0.0 | 27.0 | 188383.0 | 0.003668 | 0.236835 | 0.0 | 0.0 | 0.0 | 0.0 | 27.0 | 188383.0 | 25559.405679 | 1.534908e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 119927029.0 | 188383.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 188383.0 | 25559.405679 | 1.534908e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 119927029.0 | 188383.0 | 25559.405679 | 1.534908e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 119927029.0 |
| normal | 164457.0 | 6.0 | 0.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 164457.0 | 58148.334805 | 1.424615e+06 | 7.0 | 14.0 | 57859.0 | 73380.00 | 117511126.0 | 164457.0 | 2.459804 | 2.466897 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 164457.0 | 3.541515 | 1.553819 | 2.0 | 2.0 | 5.0 | 5.0 | 8.0 | 164457.0 | 47.981515 | 48.193465 | 0.0 | 0.0 | 72.0 | 96.0 | 100.0 | 164457.0 | 38.461665 | 38.756286 | 0.0 | 0.0 | 72.0 | 78.0 | 100.0 | 164457.0 | 33.978535 | 34.284204 | 0.0 | 0.0 | 44.0 | 68.0 | 72.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 9.749339 | 9.753269 | 0.0 | 0.0 | 18.0 | 19.2 | 20.0 | 164457.0 | 14.990621 | 15.069873 | 0.0 | 0.0 | 21.786846 | 29.852973 | 31.496031 | 164457.0 | 24.458685 | 24.926832 | 0.0 | 0.0 | 44.0 | 50.0 | 72.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 7.558702 | 7.572162 | 0.0 | 0.0 | 12.000 | 15.6 | 16.666667 | 164457.0 | 11.102558 | 11.203735 | 0.0 | 0.0 | 20.51341 | 22.733236 | 29.330303 | 164457.0 | 1164.924351 | 1180.243370 | 0.0 | 0.0 | 1.803155 | 2355.486778 | 3057.083121 | 164457.0 | 71186.095216 | 78249.038332 | 0.102118 | 136.278772 | 172.962502 | 142857.142857 | 285714.285714 | 164457.0 | 6053.155641 | 129522.082678 | 7.0 | 14.0 | 6424.000000 | 8153.222222 | 1.068283e+07 | 164457.0 | 13498.581455 | 429158.498273 | 0.0 | 0.0 | 11627.964869 | 13719.697409 | 3.540754e+07 | 164457.0 | 43585.339639 | 1.423458e+06 | 7.0 | 14.0 | 36986.0 | 43238.00 | 117440559.0 | 164457.0 | 16.784807 | 9.151835 | 4.0 | 11.0 | 14.0 | 22.0 | 2064.0 | 164457.0 | 34062.665639 | 34647.273340 | 0.0 | 0.0 | 50543.0 | 66317.0 | 156975.0 | 164457.0 | 8727.146386 | 8898.843536 | 0.0 | 0.0 | 13176.00 | 16922.000 | 39243.75 | 164457.0 | 10256.294977 | 10501.460605 | 0.0 | 0.0 | 14713.764316 | 19285.950368 | 68549.944913 | 164457.0 | 23079.325629 | 23513.684397 | 0.0 | 0.0 | 37016.0 | 43284.00 | 141872.0 | 164457.0 | 1221.682902 | 1431.210742 | 0.0 | 0.0 | 1239.0 | 2082.00 | 19406.0 | 164457.0 | 55162.797917 | 1.424580e+06 | 7.0 | 14.0 | 54144.0 | 67067.00 | 117511126.0 | 164457.0 | 11443.127877 | 203580.924270 | 7.0 | 14.0 | 11946.6 | 16542.2500 | 1.678730e+07 | 164457.0 | 17682.778109 | 537956.338481 | 0.0 | 0.0 | 14333.896201 | 18761.503636 | 4.438392e+07 | 164457.0 | 43794.498726 | 1.423459e+06 | 7.0 | 14.0 | 37471.0 | 43664.00 | 117440559.0 | 164457.0 | 1716.652025 | 2087.730052 | 7.0 | 14.0 | 103.0 | 3182.00 | 13849.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 79.039846 | 79.159871 | 0.0 | 0.0 | 136.0 | 160.0 | 192.0 | 164457.0 | 121.004080 | 57.193933 | 64.0 | 64.0 | 176.0 | 176.0 | 264.0 | 164457.0 | 33.152261 | 33.689838 | 0.0 | 0.0 | 0.041677 | 67.281168 | 96.677516 | 164457.0 | 71152.942954 | 78279.141975 | 0.068079 | 68.809865 | 100.446989 | 142857.142857 | 285714.285714 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 34.897913 | 34.918514 | 0.0 | 0.0 | 66.0 | 68.0 | 72.0 | 164457.0 | 7.857901 | 7.856737 | 0.0 | 0.0 | 13.384615 | 15.636364 | 15.818182 | 164457.0 | 12.197651 | 12.195516 | 0.0 | 0.0 | 23.027297 | 24.371369 | 24.491928 | 164457.0 | 297.512394 | 297.490619 | 0.0 | 0.0 | 530.256410 | 593.963636 | 599.854545 | 164457.0 | 0.500106 | 0.500002 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 164457.0 | 0.499894 | 0.500002 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.500106 | 0.500002 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.500283 | 0.500427 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 | 164457.0 | 8.643638 | 8.642374 | 0.0 | 0.0 | 14.50 | 17.2 | 17.400000 | 164457.0 | 9.749339 | 9.753269 | 0.0 | 0.0 | 18.0 | 19.2 | 20.0 | 164457.0 | 7.558702 | 7.572162 | 0.0 | 0.0 | 12.000 | 15.6 | 16.666667 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 2.459804 | 2.466897 | 0.0 | 0.0 | 4.0 | 5.0 | 6.0 | 164457.0 | 47.981515 | 48.193465 | 0.0 | 0.0 | 72.0 | 96.0 | 100.0 | 164457.0 | 3.541515 | 1.553819 | 2.0 | 2.0 | 5.0 | 5.0 | 8.0 | 164457.0 | 38.461665 | 38.756286 | 0.0 | 0.0 | 72.0 | 78.0 | 100.0 | 164457.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 164457.0 | 83.918684 | 0.996691 | 83.0 | 83.0 | 83.0 | 85.0 | 85.0 | 164457.0 | 1.000213 | 1.000003 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.003867 | 0.287735 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 0.003867 | 0.287735 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 164457.0 | 0.003867 | 0.287735 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 164457.0 | 20526.033231 | 1.423597e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 117440559.0 | 164457.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 164457.0 | 20526.033231 | 1.423597e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 117440559.0 | 164457.0 | 20526.033231 | 1.423597e+06 | 0.0 | 0.0 | 0.0 | 0.0 | 117440559.0 |
In [25]:
import seaborn as sns
import matplotlib.pyplot as plt
for col in df.select_dtypes(include='number').columns:
plt.figure(figsize=(12, 4))
sns.boxplot(y='Label', x=col, data=df)
plt.title(f'Boxplot of {col} by Label')
plt.show()
In [26]:
for col in df.select_dtypes(include='number').columns:
plt.figure(figsize=(12, 4))
sns.violinplot(y='Label', x=col, data=df)
plt.title(f'Violin plot of {col} by Label')
plt.show()
In [33]:
from scipy.stats import ttest_ind
label_values = df['Label'].unique()
group1 = df[df['Label'] == label_values[0]]
group2 = df[df['Label'] == label_values[1]]
for col in df.select_dtypes(include='number').columns:
stat, p = ttest_ind(group1[col], group2[col], nan_policy='omit')
print(f'{col}: t={stat:.2f}, p={p:.4f}')
Protocol: t=nan, p=nan Flow Duration: t=-21.20, p=0.0000 Tot Fwd Pkts: t=0.35, p=0.7259 Tot Bwd Pkts: t=-0.60, p=0.5465 TotLen Fwd Pkts: t=0.32, p=0.7456 TotLen Bwd Pkts: t=0.40, p=0.6891 Fwd Pkt Len Max: t=0.53, p=0.5948 Fwd Pkt Len Min: t=nan, p=nan Fwd Pkt Len Mean: t=-0.00, p=0.9984 Fwd Pkt Len Std: t=0.38, p=0.7070 Bwd Pkt Len Max: t=0.63, p=0.5314 Bwd Pkt Len Min: t=nan, p=nan Bwd Pkt Len Mean: t=0.83, p=0.4080 Bwd Pkt Len Std: t=0.65, p=0.5180 Flow Byts/s: t=73.69, p=0.0000 Flow Pkts/s: t=37.37, p=0.0000 Flow IAT Mean: t=-25.84, p=0.0000 Flow IAT Std: t=-9.07, p=0.0000 Flow IAT Max: t=-6.88, p=0.0000 Flow IAT Min: t=-11.43, p=0.0000 Fwd IAT Tot: t=-316.62, p=0.0000 Fwd IAT Mean: t=-315.29, p=0.0000 Fwd IAT Std: t=-283.50, p=0.0000 Fwd IAT Max: t=-273.95, p=0.0000 Fwd IAT Min: t=-108.79, p=0.0000 Bwd IAT Tot: t=-21.18, p=0.0000 Bwd IAT Mean: t=-35.93, p=0.0000 Bwd IAT Std: t=-6.34, p=0.0000 Bwd IAT Max: t=-7.33, p=0.0000 Bwd IAT Min: t=-333.73, p=0.0000 Fwd PSH Flags: t=nan, p=nan Bwd PSH Flags: t=nan, p=nan Fwd URG Flags: t=nan, p=nan Bwd URG Flags: t=nan, p=nan Fwd Header Len: t=0.25, p=0.7998 Bwd Header Len: t=-0.40, p=0.6892 Fwd Pkts/s: t=73.45, p=0.0000 Bwd Pkts/s: t=37.32, p=0.0000 Pkt Len Min: t=nan, p=nan Pkt Len Max: t=-0.26, p=0.7954 Pkt Len Mean: t=0.37, p=0.7113 Pkt Len Std: t=0.28, p=0.7759 Pkt Len Var: t=0.57, p=0.5680 FIN Flag Cnt: t=0.06, p=0.9483 SYN Flag Cnt: t=0.01, p=0.9946 RST Flag Cnt: t=nan, p=nan PSH Flag Cnt: t=nan, p=nan ACK Flag Cnt: t=-0.01, p=0.9946 URG Flag Cnt: t=nan, p=nan CWE Flag Count: t=nan, p=nan ECE Flag Cnt: t=nan, p=nan Down/Up Ratio: t=-0.03, p=0.9793 Pkt Size Avg: t=0.37, p=0.7107 Fwd Seg Size Avg: t=-0.00, p=0.9984 Bwd Seg Size Avg: t=0.83, p=0.4080 Fwd Byts/b Avg: t=nan, p=nan Fwd Pkts/b Avg: t=nan, p=nan Fwd Blk Rate Avg: t=nan, p=nan Bwd Byts/b Avg: t=nan, p=nan Bwd Pkts/b Avg: t=nan, p=nan Bwd Blk Rate Avg: t=nan, p=nan Subflow Fwd Pkts: t=0.35, p=0.7259 Subflow Fwd Byts: t=0.32, p=0.7456 Subflow Bwd Pkts: t=-0.60, p=0.5465 Subflow Bwd Byts: t=0.40, p=0.6891 Init Fwd Win Byts: t=nan, p=nan Init Bwd Win Byts: t=1.83, p=0.0675 Fwd Act Data Pkts: t=-0.04, p=0.9661 Fwd Seg Size Min: t=nan, p=nan Active Mean: t=-1.34, p=0.1815 Active Std: t=nan, p=nan Active Max: t=-1.34, p=0.1815 Active Min: t=-1.34, p=0.1815 Idle Mean: t=-0.29, p=0.7725 Idle Std: t=nan, p=nan Idle Max: t=-0.29, p=0.7725 Idle Min: t=-0.29, p=0.7725
/home/ics-security/anaconda3/envs/DATN/lib/python3.9/site-packages/scipy/stats/_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable. res = hypotest_fun_out(*samples, **kwds)
In [ ]:
# from sklearn.preprocessing import LabelEncoder
# # Create and fit the encoder
# le = LabelEncoder()
# df['Label_encoded'] = le.fit_transform(df['Label'])
# # Create mapping dictionary
# label_mapping = dict(zip(le.classes_, le.transform(le.classes_)))
# print("Label to Integer Mapping:")
# for label, code in label_mapping.items():
# print(f"'{label}' → {code}")
Label to Integer Mapping: 'mitm' → 0 'mitm_36plc' → 1 'normal' → 2
In [ ]:
# reverse_mapping = dict(zip(le.transform(le.classes_), le.classes_))
# print("\nInteger to Label Mapping:")
# for code, label in reverse_mapping.items():
# print(f"{code} → '{label}'")
Integer to Label Mapping: 0 → 'mitm' 1 → 'mitm_36plc' 2 → 'normal'
In [ ]: